Nothing
context('test-blr-confusion-matrix')
model <- glm(
honcomp ~ female + read + science, data = hsb2,
family = binomial(link = "logit")
)
test_that("prints the correct output", {
k <- blr_confusion_matrix(model, cutoff = 0.4)
x <- cat("Confusion Matrix and Statistics
Reference
Prediction 0 1
0 125 16
1 22 37
Accuracy : 0.8100
No Information Rate : 0.7350
Kappa : 0.5293
McNemars's Test P-Value : 0.4173
Sensitivity : 0.6981
Specificity : 0.8503
Pos Pred Value : 0.6271
Neg Pred Value : 0.8865
Prevalence : 0.2650
Detection Rate : 0.1850
Detection Prevalence : 0.2950
Balanced Accuracy : 0.7742
Precision : 0.6271
Recall : 0.6981
'Positive' Class : 1")
expect_output(print(k), x)
})
test_that('throws an error when the model is not of the class glm', {
model <- lm(read ~ female + science, data = hsb2)
error <- cat("Hmmm.. Looks like you have specified an incorrect model. The below steps might be helpful:\n\n* Check if you have used the glm() function to build the model.\n* If you have never used it before, you can learn more by typing ?glm or help(glm) in the Console.\n\n Please specify the model in the below format:\n\n glm( honcomp ~ female + read + science , data = hsb2 , family = binomial(link = 'logit'))\n\n Happy modeling :)")
expect_error(blr_confusion_matrix(model), error)
})
test_that('cutoff value is between 0 and 1', {
error <- cat('cutoff can take on values between 0 and 1 only. You have used 1.4 , please specify a value between 0 and 1 only.')
expect_error(blr_confusion_matrix(model, 1.4), error)
})
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